@article{BartholdWiesmeierBreueretal.2013, author = {Barthold, Frauke Katrin and Wiesmeier, Martin and Breuer, L. and Frede, Hans-Georg and Wu, J. and Blank, F. Benjamin}, title = {Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia}, series = {Journal of arid environments}, volume = {88}, journal = {Journal of arid environments}, number = {1}, publisher = {Elsevier}, address = {London}, issn = {0140-1963}, doi = {10.1016/j.jaridenv.2012.08.004}, pages = {194 -- 205}, year = {2013}, abstract = {The spatial distribution of soil types is controlled by a set of environmental factors such as climate, organisms, parent material and topography as well as time and space. A change of these factors will lead to a change in the spatial distribution of soil types. In this study, we use a digital soil mapping approach to improve our knowledge about major soil type distributing factors in the steppe regions of Inner Mongolia (China) which currently undergo tremendous environmental change, e.g. climate and land use change. We use Random Forests in an effort to map Reference Soil Groups according to the World Reference Base for Soil Resources (WRB) in the Xilin River catchment. We benefit from the superior prediction capabilities of RF and additional interpretive results in order to identify the major environmental factors that control spatial patterns of soil types. The nine WRB soil groups that were identified and spatially predicted for the study area are Arenosol, Calcisol, Cambisol, Chernozem, Cryosol, Gleysol, Kastanozem, Phaeozem and Regosol. Model and prediction performances of the RF model are high with an Out-of-Bag error of 51.6\% for the model and a misclassification error for the predicted map of 28.9\%. The main controlling factors of soil type distribution are land use, a set of topographic variables, geology and climate. However, land use and climate are of major importance and topography and geology are of minor importance. The visualizations of the predictions, the variable importance measures as result of RF and the comparisons of these with the spatial distribution of the environmental factors delivered additional, quantitative information of these controlling factors and revealed that intensively grazed areas are subjected to soil degradation. However, most of the area is still governed by natural soil forming processes which are driven by climate, topography and geology. Most importantly though, our study revealed that a shift towards warmer temperatures and lower precipitation regimes will lead to a change of the spatial distribution of RSGs towards steppe soils that store less carbon, i.e. a decrease of spatial extent of Phaeozems and an increase of spatial extent of Chernozems and Kastanozems.}, language = {en} } @article{SchudomaLarhlimiWalther2011, author = {Schudoma, Christian and Larhlimi, Abdelhalim and Walther, Dirk}, title = {The influence of the local sequence environment on RNA loop structures}, series = {RNA : a publication of the RNA Society}, volume = {17}, journal = {RNA : a publication of the RNA Society}, number = {7}, publisher = {Cold Spring Harbor Laboratory Press}, address = {Cold Spring Harbor, NY}, issn = {1355-8382}, doi = {10.1261/rna.2550211}, pages = {1247 -- 1257}, year = {2011}, abstract = {RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized eta/theta-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55\%, thus significantly better than expected by chance (17\%-25\%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure.}, language = {en} } @misc{SultanaSiegKellermannetal.2018, author = {Sultana, Zakia and Sieg, Tobias and Kellermann, Patric and M{\"u}ller, Meike and Kreibich, Heidi}, title = {Assessment of business interruption of flood-affected companies using random forests}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {939}, issn = {1866-8372}, doi = {10.25932/publishup-45977}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459778}, pages = {18}, year = {2018}, abstract = {Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.}, language = {en} } @article{SultanaSiegKellermannetal.2018, author = {Sultana, Zakia and Sieg, Tobias and Kellermann, Patric and M{\"u}ller, Meike and Kreibich, Heidi}, title = {Assessment of business interruption of flood-affected companies using random forests}, series = {Water}, volume = {10}, journal = {Water}, number = {8}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w10081049}, pages = {16}, year = {2018}, abstract = {Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.}, language = {en} }